Hire Generative AI DevelopersWork with engineers who build real products on top of generative AI — copilots, content tools, and custom GenAI features — grounded in your data, not a generic wrapper around a public API.
A generative AI developer specializes in building products around large language and diffusion models — text, image, audio, and code generation — rather than traditional predictive ML. That means deep, hands-on experience with prompt design, fine-tuning, retrieval-augmented generation, output evaluation, and the guardrails that keep a generative feature accurate and on-brand instead of confidently wrong. At Apptechies, our generative AI developers have shipped real consumer and enterprise GenAI products, not just API demos.
We design retrieval pipelines that ground LLMs in your proprietary documents, databases, and APIs with verified source citations.
Custom recursive text splitting and high-density vector embeddings (text-embedding-3-large, Cohere) that retain contextual nuance.
Combining dense vector search with sparse keyword search (BM25) inside pgvector or Pinecone for maximum retrieval accuracy.
Filtering top-k retrieved chunks through a reranker model to eliminate noise before passing context to the LLM.
Using structured outputs (Pydantic / Instructor) and automated guardrails to eliminate hallucination and formatting drift.
From prompt engineering to full model fine-tuning.
Adapting foundation models to your domain vocabulary, tone, and task — via fine-tuning, few-shot prompting, or both.
Conversational interfaces that understand context, hold state across a session, and hand off to a human when needed.
Diffusion-model integrations for image, avatar, and design generation, tuned for consistent style and quality.
Internal or customer-facing tools that generate code, copy, or structured content against your own templates and standards.
Connecting LLMs to your own documents and data so answers are grounded in fact, with citations back to source.
Automated evaluation suites, content filtering, and fallback logic that catch bad output before it reaches a user.
LLM API fees compound rapidly at scale. We architect cost-efficient pipelines that keep your gross margins protected.
Serving identical or semantically similar queries directly from Redis/vector cache, eliminating 30–60% of LLM API calls.
Routing simple queries to lightweight models (GPT-4o-mini, Claude 3.5 Haiku) and reserving expensive reasoning models (o1, Sonnet) for complex tasks.
Compressing system prompts and selectively filtering message history to minimize per-token inference overhead.
Foundation models, vector stores, and fine-tuning frameworks our GenAI engineers deploy.
Configured using zero-data-retention enterprise API endpoints with automatic pre-call PII masking.
All custom weights, LoRA adapters, embeddings, and code are 100% assigned to your company under mutual NDA.
The questions we hear most from teams hiring a generative ai developer. Don't see yours? Ask us directly on the right.
An AI developer covers the full spectrum of machine learning, including predictive models and classic ML. A generative AI developer specializes specifically in large language and diffusion models — text, image, and content generation — and the fine-tuning, prompting, and RAG techniques unique to that work.
Yes — this is one of the most common reasons teams hire us. We architect retrieval-augmented generation, structured output validation, and automated evaluation pipelines specifically to catch and reduce hallucinated or inaccurate responses before they reach your users.
Both, depending on your use case and budget. Many products are best served by prompting a foundation model well; others need a fine-tuned or open-source model for cost, latency, or data-privacy reasons. We’ll recommend the right approach after understanding your constraints.
Through model routing (cheaper models for simple tasks, stronger models only when needed), response caching, prompt-length optimization, and monitoring that flags cost spikes before they become a bill shock.
Yes — most of our GenAI engagements are exactly this: adding a copilot, content generator, or intelligent search feature into an existing web or mobile app, integrated with your current backend and auth.
Typically within 3-5 business days for a shortlist and one to two weeks to full onboarding, depending on the complexity of your systems and any NDA or procurement steps on your side.
Yes — we design with data residency, PII redaction, and audit logging in mind from the start, particularly important for healthcare, fintech, and enterprise engagements subject to GDPR or HIPAA.
Also Exploring?
Tell us what you're building, and a senior engineer or solutions architect will reply within 24 hours.
